A framework for positioning, navigation, and tracking problems using particle filters
(sequential Monte Carlo methods) is developed. It consists of a class of motion models and …

We propose a methodology to sample sequentially from a sequence of probability
distributions that are defined on a common space, each distribution being known up to a …

Probabilistic inference is the problem of estimating the hidden variables (states or
parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete …

The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …

We develop methods for performing smoothing computations in general state-space models.
The methods rely on a particle representation of the filtering distributions, and their evolution …

Abstract The term “sequential Monte Carlo methods” or, equivalently,“particle filters,” refers
to a general class of iterative algorithms that performs Monte Carlo approximations of a …

Particle filters for dynamic state-space models handling unknown static parameters are
discussed. The approach is based on marginalizing the static parameters out of the posterior …

This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State
estimation for nonlinear systems has been a challenge encountered in a wide range of …

Tracking objects involves the modeling of non-linear non-Gaussian systems. On one hand,
variants of Kalman filters are limited by their Gaussian assumptions. On the other hand …

This article considers how to implement Markov chain Monte Carlo (MCMC) moves within a
particle filter. Previous, similar, attempts have required the complete history (“trajectory”) of …